ggdist

name: ggdist description: R ggdist package for distribution visualizations. Use for visualizing distributions and uncertainty.

Visualizations of distributions and uncertainty.

Stat Halfeye

library(ggplot2)
library(ggdist)

ggplot(data, aes(x = group, y = value)) +
  stat_halfeye()

# With options
ggplot(data, aes(x = group, y = value)) +
  stat_halfeye(
    .width = c(0.66, 0.95),
    point_interval = median_qi
  )

Stat Eye

# Full eye (violin + interval)
ggplot(data, aes(x = group, y = value)) +
  stat_eye()

Stat Slab

# Just the density
ggplot(data, aes(x = group, y = value)) +
  stat_slab()

# Horizontal
ggplot(data, aes(y = group, x = value)) +
  stat_slab()

Stat Interval

# Just intervals
ggplot(data, aes(x = group, y = value)) +
  stat_interval()

# Multiple intervals
ggplot(data, aes(x = group, y = value)) +
  stat_interval(.width = c(0.5, 0.8, 0.95))

Stat Pointinterval

# Point + interval
ggplot(data, aes(x = group, y = value)) +
  stat_pointinterval()

# Different point types
ggplot(data, aes(x = group, y = value)) +
  stat_pointinterval(point_interval = mean_qi)

Stat Dots

# Dot plot
ggplot(data, aes(x = group, y = value)) +
  stat_dots()

# Quantile dots
ggplot(data, aes(x = group, y = value)) +
  stat_dotsinterval()

Gradient Intervals

ggplot(data, aes(x = group, y = value)) +
  stat_gradientinterval()

CCDF Bars

# Complementary CDF
ggplot(data, aes(x = group, y = value)) +
  stat_ccdfinterval()

Combining

ggplot(data, aes(x = group, y = value)) +
  stat_halfeye(
    adjust = 0.5,
    width = 0.6,
    .width = 0,
    justification = -0.2,
    point_colour = NA
  ) +
  geom_boxplot(
    width = 0.15,
    outlier.shape = NA
  ) +
  stat_dots(
    side = "left",
    justification = 1.1,
    binwidth = 0.25
  )

With Posterior Samples

# From Bayesian models
library(brms)
posterior <- as_draws_df(fit)

ggplot(posterior, aes(x = b_Intercept)) +
  stat_halfeye()